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DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Analysis of Energy Savings and CO2 Emission Reduction Contribution for Industrial Facilities in USA

Abstract Energy audits can identify energy consumptions, energy costs of the facility and evolve to develop measures to maximize efficiency, optimize supply energy, and eliminate waste. This paper investigates the potential energy savings at 20 different industrial sectors with 152 assessments for various facilities in Wisconsin, USA. On average, eight energy recommendations were suggested and applied in each facility. This paper provides a detailed guideline for each industry in terms of eight different energy categories: heating, ventilation, and air conditioning (HVAC) systems, heat recovery systems, electrical demand management, and utility bills, motors, compressors, waste management, and productivity enhancement, lighting, besides building envelope. In total, the energy savings were as follows: 98 million kWh in the shape of electricity, 561 billion British thermal units (BTUs) gas savings, 44 million gallons water savings, and 2-million-pound solid waste savings. Based on these savings, a 100-thousand-ton reduction in carbon dioxide emissions was obtained.

Energy & Fuels↗

DOE OSTI2022

Air-to-Liquid Heat Exchanger Fabricated Using Deposition-Based Additive Manufacturing Processes

Abstract This work describes the use of deposition-based additive manufacturing (AM) techniques to fabricate air-cooled, two-fluid heat exchangers. The project focused on a Heating, Ventilation and Air Conditioning application and used an industry-standard copper/aluminum heat exchanger manufactured with conventional technology as the basis for assessing performance. The manufacturing constraints associated with using deposition-based AM technology for this application include the need for a continuous tool path within each build layer that allows uninterrupted extrusion and therefore defect-free water channel walls that correspond to a reliably leak-tight heat exchanger. A geometry that respects these constraints was developed, simulated, optimized and finally manufactured and tested. The material used was a composite of polymer filled with conductive flakes in order to provide high conductivity in the direction that heat must flow in this heat exchanger, across the wall separating the fluids. The measured performance for several test coupons matched the predicted performance and the test coupons exhibited performance that approach and in some cases exceeds conventional technology

Engineering↗

DOE OSTI2022

Analyzing the Multiscale Impacts of Implementing Energy-Efficient HVAC Improvements Through Energy Audits and Economic Input–Output Analysis

Abstract Heating, ventilation, and air-conditioning (HVAC) systems are usually an industry’s highest consumer of energy, most of which goes toward space cooling in buildings. Industrial energy-efficiency audits not only benefit manufacturers but also generate significant economic and environmental benefits to localities, states, and the nation. This article analyzes the micro- and macro scale impacts of implementing energy-efficient HVAC systems by integrating the industrial building energy data with the macroeconomic regional economic flow model. Micro-scale data include 10 years of historical energy, cost, and carbon dioxide savings achieved from energy-efficient HVAC implementation offered to manufacturers through industrial energy audits. The data were integrated into the macroeconomic modeling framework to illuminate the cascading regional economic impacts of implementing energy-efficient HVAC recommendations in manufacturing facilities. Results show that if recommendations had been implemented throughout all manufacturers in the region, $656 M energy costs would have been directly saved, 7.8 million metric tons of carbon dioxide emissions would have been avoided, and 4387 jobs could have been created, resulting in a total annual economic impact of $899 M stemming from direct, indirect, and induced impacts. The results offer insight into how industrial energy systems can be designed and provide models for how communities can accomplish a net-zero society.

Energy & Fuels↗

DOE OSTI2023

PHASE CHANGE MATERIAL FOR THERMAL ENERGY STORAGE IN BUILDINGS BASED ON SODIUM SULFATE DECAHYDRATE AND DISODIUM HYDROGEN PHOSPHATE DODECAHYDRATE

The worldwide increasing energy demand and 2050 net zero carbon target urge the globe to solve the energy challenge. Thermal Energy Storage (TES) has received significant attention in recent years as TES can be integrated into heating, ventilation, and air-conditioning systems where the energy would be stored during low-demand times and dispatched during high-demand times, resulting in controlling the peak load and improving energy savings. Material development is an integral part of TES. Salt hydrates are appealing due to cost-effectiveness, low- to no toxicity, and their high melting enthalpy, where energy is stored as latent heat. However, most salt hydrates are prone to incongruent melting (i.e., phase separation upon melting), which results in poor stability and large supercooling. In this study, we produced a highly stable novel energy storage material at a composition of 32 wt% sodium sulfate decahydrate, 52 wt% sodium phosphate dibasic dodecahydrate, 12 wt% milled expanded graphite, and 4 wt% borax. The material has a melting temperature of 28°C and an energy storage capacity of 167 kJ/kg with a supercooling of less than 3°C. The system showed no loss in energy storage performance after 150 cycles. The findings suggest that the novel energy storage material developed in this might be utilized in building heating and cooling applications.

Turnaoglu, Tugba↗

DOE OSTI2025

UrbanScaping: Community Spatial Data Visualization & Analytics

Evaluating the electrification potential of buildings through retrofitting is crucial for reducing carbon emissions and the carbon footprint of built environments. This study leverages the Automatic Building Energy Modeling (AutoBEM) software, integrating the Model America database to create an urban context-based spatial analysis platform for community engagement and development. We selected Camp Hill Borough, PA, as a case study to analyze building-specific energy performance and evaluate the electrification potential of each building by switching to different Heating, Ventilation, and Air Conditioning (HVAC) systems and measurement components. The simulation results generated by the workflow provide retrofitting suggestions to help mitigate the carbon footprint as well as energy saving statistics of buildings. Additionally, the developed web-based interface serves as a community engagement platform, allowing residents to provide feedback and further develop interactive communication protocols. The outcomes of this project offer a baseline for community electrification planning and contribute to the design of low-carbon communities.

Chowdhury, Shovan [ORNL]↗

DOE OSTI2022

EXPERIMENTAL AND MODELLING ANALYSIS OF A LARGE-SCALE TWO-PHASE LOOP THERMOSYPHON

Liquid pumping requires large quantities of electrical energy, including about 7% of the energy of building heating, ventilation, and air conditioning (HVAC) systems. To replace pumped condenser-cooling tower water loops with a passive alternative system, we implemented a commercial-scale two-phase loop thermosyphon (TPLT). The unit consists of a 13 m riser integrated with a commercially available cooling tower and circulation water heater that simulates heat loads up to 25 kW. In addition to providing passive cooling capabilities, the cooling tower unit is also maintenance free, reliable, and can operate in both dry and wet modes. This study characterizes the performance (temperature difference between the evaporator and condenser) and the two-phase flow behavior of the loop under various refrigerant charges. Sight glasses installed throughout the loop are used to identify the operating flow regimes in the riser and downcomer. Over the range of operating conditions, we identified that there is an optimal refrigerant charge range for a specific heat load at which near-isothermal operation can be achieved. We further developed a model to predict the operating flow rate and gravitational height in the downcomer and compared it with the experimental data. The results show that the model agrees with the experimental data, in particular the threshold gravitational head height at which there will be subcooled liquid at the condenser exit, indicating that it can be used as a predictive tool for identifying the optimal loop charge for a given heat load.

Loop thermosyphon, passive two phase↗

DOE OSTI2020

Surface reservoirs dominate dynamic gas-surface partitioning of many indoor air constituents

Human health is affected by indoor air quality. One distinctive aspect of the indoor environment is its very large surface area that acts as a poorly characterized sink and source of gas-phase chemicals. In this work, air-surface interactions of 19 common indoor air contaminants with diverse properties and sources were monitored in a house using fast-response, on-line mass spectrometric and spectroscopic methods. Enhanced-ventilation experiments demonstrate that most of the contaminants reside in the surface reservoirs and not, as expected, in the gas phase. They participate in rapid air-surface partitioning that is much faster than air exchange. Phase distribution calculations are consistent with the observations when assuming simultaneous equilibria between air and large weakly polar and polar absorptive surface reservoirs, with acid-base dissociation in the polar reservoir. Chemical exposure assessments must account for the finding that contaminants that are fully volatile under outdoor air conditions instead behave as semivolatile compounds indoors.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2020

Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control

Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.

Kurte, Kuldeep↗

DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗

DOE OSTI2021

Comparative analysis of model-free and model-based HVAC control for residential demand response

In this paper, we present a comparative analysis of model-free reinforcement learning (RL) and model predictive control (MPC) approaches for intelligent control of heating, ventilation, and air-conditioning (HVAC). Deep-Q-network (DQN) is used as a candidate for model-free RL algorithm. The two control strategies were developed for residential demand-response (DR) HVAC system. We considered MPC as our golden standard to compare DQN's performance. The question we tried to answer through this work was, What % of MPC's performance can be achieved by model-free RL approach for intelligent HVAC control?. Based on our test result, RL achieved an average of ≈ 62% daily cost saving of MPC. Considering the pure optimization and model-based nature of MPC methods, the RL showed very promising performance. We believe that the interpretations derived from this comparative analysis provide useful insights to choose from various DR approaches and further enhance the performance of the RL-based methods for building energy managements.

Kurte, Kuldeep↗

DOE OSTI2020

OpenStudio System-level KPI Reporting Measure v1.0

This is a OpenStudio measure that calculates and reports the system-level key performance indicators (KPIs) for evaluating building energy performance at the system level. The systems are defined by the service it provides in commercial buildings, which include (1) lighting, (2) Miscellaneous Electric Loads (MELs), (3) Heating, Ventilation and Air Conditioning (HVAC), and (4) Service Water Heating (SWH).

Li, Han↗

DOE OSTI2021

Functional Mock-up Interface - Machine Learning Center (FMI-MLC) v1

The Functional Mock-up Interface - Machine Learning Center is a tool to interface the Python-Tensorflow library with building simulation models, to develop and train machine learning-based controls. In particular, this framework allows the use of reinforcement learning through the standardized Functional Mock-up Interface, an open industry standard for co-simulation. First application was the development of a controller for dynamic façade and heating ventilation and air conditioning (HVAC) systems.

Gehbauer, Christoph↗

DOE OSTI2021

Weatherization Assistant NEAT/MHEA

The software provides a measure selection technique indicating cost effective retrofit activities that can be applied to a home using a standard Savings to Investment Ratio (SIR). Users must provide an input file describing the characteristics of the home to be evaluated. The software takes the input data provided and calculates energy savings and cost savings predicted for a standard set of measures given the input parameters. The Weatherization Assistant computes estimates of pre-retrofit whole building space heating and cooling energy consumptions based on the house description data supplied by the user. The consumptions are computed using a monthly heating and cooling variable base degree-day method by algorithms similar to those developed for the CIRA program [LBL, 1982]. The building consumptions are needed in computing the energy savings from measures affecting the efficiencies of the heating and cooling equipment. Weatherization Assistant then computes the energy savings and costs for each individual measure applicable to the building described as if it were the only measure installed in the house. From these energy savings, a discounted dollar savings over the life of each measure is computed. The ratio of this dollar savings to the cost of installing the measure, the "savings-to-investment ratio" (SIR), is used in an initial ranking of the measures' effectiveness. The "interacted" savings and SIR of measures are then determined assuming the measures are added to the house collectively, in order of their ranking, e.g., the second ranked measure is installed in the house initially described by the user after having been modified by the first ranked measure. If this second-ranked measure's updated SIR is greater than a user-defined limit, the measure is left implemented, else it is removed so that the next measure's effectiveness is not dependent on it. The choice between two mutually exclusive measures (such as different levels of insulation) is made on the basis of their "net present value" (NPV), the difference of life-time savings and installation cost, rather than their SIR. This has been shown to be the more correct criterion on which to base the selection between two measures, both of which cannot be installed. The audit computes and reports to the user the energy savings, discounted dollar savings, installation cost, and SIR for each measure considered cost-effective. For those with SIR greater than the user-designated cutoff, a materials list gives the material name, type, and quantity required for installation of the measure. Weatherization Assistant permits entry of pre-retrofit billing data for gas or electrically heated homes or homes with electric air-conditioning. The user may then make the decision to have the savings of the measures adjusted to reflect the difference in billed consumption and that predicted by the program.

Gettings, Michael↗

DOE OSTI2022

Development of a simplified calibrated building simulation model of a supermarket for proposed ECMs and control strategies impact evaluation

Calibrated building energy simulation is an important pathway to more energy-efficient buildings, but the information requirements of some approaches to this problem are significant. This is particularly true for supermarkets and other so-called “big-box” retail stores. Another characteristic of supermarkets is the significant interaction between Heating Ventilating and Air Conditioning (HVAC) and refrigeration systems in these buildings. These buildings could contain a wide variety of systems and a degree of load diversity that makes calibrated modeling a challenge. This paper describes a simplified approach that uses OpenStudio and EnergyPlus to combine known building parameters with “typical” parameters, resulting in a simplified building that is amenable to calibration. This approach was applied to a big-box store located in Nashville, Tennessee, and a calibrated model was obtained that was used to study potential energy conservation measures. Further, the paper also explores the capabilities of whole-building energy modeling tools, such as EnergyPlus, for modeling the HVAC controls and sequences and their impact evaluation. Although some measures are precluded by the model simplicity, several measures were found to improve the efficiency of the model and demonstrate that the simplified modeling approach is effective. Practical Application: This paper introduces a hybrid approach of building energy model calibration using limited information available from the actual building in combination with characteristics of a “typical” building of the same type. This hybrid approach would also be applicable for other building types than discussed in this paper to calibrate the building energy model using limited information from the actual building.

42 ENGINEERING↗

DOE OSTI2024

Competitiveness Assessment of Decarbonizing Electricity and Process Heat Supply to a Campus with a Small Nuclear Reactor

This paper analyzes the competitiveness of siting a small nuclear reactor to support decarbonization of sites requiring tens of MW of electricity and/or process heat to support centralized heating and cooling system. This paper focuses on campuses as representative of sites with collections of buildings and research facilities with decarbonization needs represented by buildings heating, and electricity consumption by electrical loads which may include cooling via chilled water (e.g., for air conditioning and to cool down computer clusters). A nuclear reactor can be considered to decarbonize a site’s high-temperature steam generation used mostly for building heating needs, climate control, and hot water, by supplying process heat capabilities, while electricity decarbonization would be achieved mostly by the grid. However, a secondary application can be considered to maximize reactor utilization and avoid ramping down the reactor if the steam demand varies significantly throughout the year. Chilled water generation through steam-driven systems was identified as an attractive secondary option for the site analyzed, due to potential for plant design simplification, while electricity generation could be considered as well to reduce electricity purchases for a wider range of site applications. For a campus with peak 60MW thermal power demand, a small nuclear reactor with similar thermal power rating would almost eliminate CO2 emissions from steam generation and reduce electricity imports for chilled water production. A preliminary techno-economic feasibility study shows that a small nuclear reactor design that is optimized to support process heat can represent an economically feasible option when compared with other decarbonization alternatives.

Stauff, Nicolas E.↗

DOE OSTI2023

Community Geothermal: Borefield Design, Thermal Conductivity, and Subsurface Modeling Data - Chicago, IL

This dataset encompasses the development of a geothermal energy system for the West Woodlawn neighborhood in Chicago, Illinois. This project is part of a broader initiative to design and deploy geothermal heating and cooling systems at a community scale. The dataset includes thermal conductivity test results, calculations for borehole sizing based on the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) method, as well as simulated thermal loads based on actual energy usage from individual buildings. Also provided here are files used for numerical modeling via COMSOL Multiphysics to simulate borefield design and subsurface thermal behavior. Two manuscripts are attached, which outline the broad objectives of the project and a description of the numerical modeling methodology and results.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Materials research and development needs to enable efficient and electrified buildings

Because of the complexity of modern buildings—with many interconnected materials, components, and systems—fully electrifying buildings will require targeted R&D and efficient coordination across those material, component, and system levels. Because buildings that consume the smallest amount of energy are easier to electrify, energy efficiency is a crucial step toward fully electrified buildings. Materials advances will play an important role in both reducing the energy intensity of buildings and electrifying their remaining energy use. Materials are currently being explored, discovered, synthesized, evaluated, optimized, and implemented across many building components, including solid-state lighting; dynamic windows and opaque envelopes; cold climate heat pumps; thermal energy storage; heating, ventilating, and air conditioning (HVAC); refrigeration; non-vapor compression HVAC; and more. In this article, we review the current state-of-the-art of materials for various buildings end uses and discuss R&D challenges and opportunities for both efficiency and electrification.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗